Association of periodontal disease with sex hormones levels among men; an analysis of NHANES III data

Background. The purpose of this study was to investigate the association of periodontal disease with sex hormones. If periodontal disease is associated with abnormal levels of sex hormones this may indicate a link between periodontal disease and prostate cancer. ^ Methods. All participants were derived from the third National Health and Nutrition Examination survey (NHANES III) data. For the purpose of our study, serum samples for hormones measurements such as testosterone, free testosterone, estradiol, free estradiol and sex hormone binding globulin (SHBG) and periodontal examination data were available for 1,101 of these men. ^ Results. After adjusting for known risk factors, periodontal disease was significantly associated with sex hormones as testosterone, free testosterone, estradiol and free estradiol. The association of periodontal disease and sex hormone levels were not significantly different between ethnicity groups. ^ Discussion. The results indicate the need for further study of periodontal disease and serum levels of testosterone, free testosterone, estradiol and free estradiol in men.^

The relationship between obesity, autoimmune disease, and C-reactive protein: A secondary data analysis of the National Health and Nutrition Examination Survey (NHANES) 2009--2010

Autoimmune diseases are a group of inflammatory conditions in which the body's immune system attacks its own cells. There are over 80 diseases classified as autoimmune disorders, affecting up to 23.5 million Americans. Obesity affects 32.3% of the US adult population, and could also be considered an inflammatory condition, as indicated by the presence of chronic low-grade inflammation. C-reactive protein (CRP) is a marker of inflammation, and is associated with both adiposity and autoimmune inflammation. This study sought to determine the cross-sectional association between obesity and autoimmune diseases in a large, nationally representative population derived from NHANES 2009–10 data, and the role CRP might play in this relationship. Overall, the results determined that individuals with autoimmune disease were 2.11 times more likely to report being overweight than individuals without autoimmune disease and that CRP had a mediating affect on the obesity-autoimmune relationship. ^

A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Quality Data is Key to Improving Education

The Data Quality Campaign (DQC) has been focused since 2005 on advocating for states to build robust state longitudinal data systems (SLDS). While states have made great progress in their data infrastructure, and should continue to emphasize this work, t data systems alone will not improve outcomes. It is time for both DQC and states to focus on building capacity to use the information that these systems are producing at every level – from classrooms to state houses. To impact system performance and student achievement, the ingrained culture must be replaced with one that focuses on data use for continuous improvement. The effective use of data to inform decisions, provide transparency, improve the measurement of outcomes, and fuel continuous improvement will not come to fruition unless there is a system wide focus on building capacity around the collection, analysis, dissemination, and use of this data, including through research.

Literature review of estimation of prevalence rate of HIV/AIDS in India using population survey and sentinel surveillance based data

HIV/AIDS is a treatable although incurable disease that presents immense challenges to those infected including physical, social and psychological effects. As of 2009, an estimated 2.4 million people were living with HIV or AIDS in India, 0.3% of the country's population. In India, it is difficult to not only treat but also to track because it is associated with socio-economic factors such as illiteracy, social biases, poor sanitation, malnutrition and social class. Nevertheless, it is important to know the prevalence of HIV/AIDS for several reasons. At the individual level, the quality of life of people living with HIV/AIDS is markedly lower than their counterparts without the disease and is associated with challenges. At the community level, it is important to identify high risk groups, monitor prevention efforts, and allocate appropriate resources to target programs for the reduction of transmission of HIV. ^

Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.

DEVELOPMENT OF NOVEL METHODS TO MINIMIZE THE IMPACT OF SEQUENCING ERRORS IN THE NEXT-GENERATION SEQUENCING DATA ANALYSIS

Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as de novo assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. . To minimize the impact of sequencing errors, we developed a highly robust and efficient method, MTM, to correct the errors in NGS reads. We demonstrated the effectiveness of MTM on both single-cell data with highly non-uniform coverage and normal data with uniformly high coverage, reflecting that MTM’s performance does not rely on the coverage of the sequencing reads. MTM was also compared with Hammer and Quake, the best methods for correcting non-uniform and uniform data respectively. For non-uniform data, MTM outperformed both Hammer and Quake. For uniform data, MTM showed better performance than Quake and comparable results to Hammer. By making better error correction with MTM, the quality of downstream analysis, such as mapping and SNP detection, was improved. SNP calling is a major application of NGS technologies. However, the existence of sequencing errors complicates this process, especially for the low coverage (

A Novel Mechanism of Skin Tumor Promotion Involving Interferon-gamma (IFNγ)/Signal Transducer and Activator of Transcription-1 (Stat1) Signaling in Epidermis

The JAK-STAT pathway is a major signaling pathway involved in many biological processes including proliferation, apoptosis, and differentiation. Aberrant expression of STATs has been reported in multiple human cancers and murine mouse models of tumorigenesis. Previous studies from our lab and others have established a critical role for Stat3 in epithelial tumorigenesis, but the role of Stat1 is largely unknown. The current study was designed to explore the role of Stat1 during multistage skin carcinogenesis. Topical treatment with both TPA and the anthrone derivative chrysarobin (CHRY) led to rapid phosphorylation of Stat1 on both tyrosine (Tyr701) and serine (Ser727) residues in epidermis. CHRY treatment also led to upregulation of unphosphorylated Stat1 (uStat1) at later time points. In addition, CHRY treatment also led to upregulation of IRF-1 mRNA and protein which was dependent on Stat1. Further analyses demonstrated that topical treatment with CHRY but not TPA upregulated interferon-gamma (IFNg) mRNA in the epidermis and that the induction of both IRF-1 and uStat1 was dependent on IFNg signaling. Stat1 deficient (Stat1-/-) mice were highly resistant to skin tumor promotion by CHRY. In contrast, the tumor response (in terms of both papillomas and squamous cell carcinomas) was similar in Stat1-/- mice and wild-type littermates with TPA as the promoter. Histological evaluation of the proliferative response confirmed the data obtained from the tumor study for both TPA and CHRY. In addition, maximal induction of both cyclooxygenase-2 and inducible nitric oxide synthase in epidermis following treatment with CHRY was also dependent on the presence of functional Stat1. Following CHRY treatment, Stat1-/- mice exhibited reduced macrophage infiltration and reduced production of many immune cell derived chemokines/cytokines. These studies define a novel mechanism associated with skin tumor promotion by the anthrone class of tumor promoters involving upregulation of IFNg signaling in the epidermis and downstream signaling through activated (phosphorylated) Stat1 and subsequent upregulation of IRF-1 and uStat1.

Understanding and Characterizing Shared Decision-Making and Behavioral Intent in Medical Uncertainty

Applying Theoretical Constructs to Address Medical Uncertainty Situations involving medical reasoning usually include some level of medical uncertainty. Despite the identification of shared decision-making (SDM) as an effective technique, it has been observed that the likelihood of physicians and patients engaging in shared decision making is lower in those situations where it is most needed; specifically in circumstances of medical uncertainty. Having identified shared decision making as an effective, yet often a neglected approach to resolving a lack of information exchange in situations involving medical uncertainty, the next step is to determine the way(s) in which SDM can be integrated and the supplemental processes that may facilitate its integration. SDM involves unique types of communication and relationships between patients and physicians. Therefore, it is necessary to further understand and incorporate human behavioral elements - in particular, behavioral intent - in order to successfully identify and realize the potential benefits of SDM. This paper discusses the background and potential interaction between the theories of shared decision-making, medical uncertainty, and behavioral intent. Identifying Shared Decision-Making Elements in Medical Encounters Dealing with Uncertainty A recent summary of the state of medical knowledge in the U.S. reported that nearly half (47%) of all treatments were of unknown effectiveness, and an additional 7% involved an uncertain tradeoff between benefits and harms. Shared decision-making (SDM) was identified as an effective technique for managing uncertainty when two or more parties were involved. In order to understand which of the elements of SDM are used most frequently and effectively, it is necessary to identify these key elements, and understand how these elements related to each other and the SDM process. The elements identified through the course of the present research were selected from basic principles of the SDM model and the “Data, Information, Knowledge, Wisdom” (DIKW) Hierarchy. The goal of this ethnographic research was to identify which common elements of shared decision-making patients are most often observed applying in the medical encounter. The results of the present study facilitated the understanding of which elements patients were more likely to exhibit during a primary care medical encounter, as well as determining variables of interest leading to more successful shared decision-making practices between patients and their physicians. Understanding Behavioral Intent to Participate in Shared Decision-Making in Medically Uncertain Situations Objective: This article describes the process undertaken to identify and validate behavioral and normative beliefs and behavioral intent of men between the ages of 45-70 with regard to participating in shared decision-making in medically uncertain situations. This article also discusses the preliminary results of the aforementioned processes and explores potential future uses of this information which may facilitate greater understanding, efficiency and effectiveness of doctor-patient consultations.Design: Qualitative Study using deductive content analysisSetting: Individual semi-structure patient interviews were conducted until data saturation was reached. Researchers read the transcripts and developed a list of codes.Subjects: 25 subjects drawn from the Philadelphia community.Measurements: Qualitative indicators were developed to measure respondents’ experiences and beliefs related to behavioral intent to participate in shared decision-making during medical uncertainty. Subjects were also asked to complete the Krantz Health Opinion Survey as a method of triangulation.Results: Several factors were repeatedly described by respondents as being essential to participate in shared decision-making in medical uncertainty. These factors included past experience with medical uncertainty, an individual’s personality, and the relationship between the patient and his physician.Conclusions: The findings of this study led to the development of a category framework that helped understand an individual’s needs and motivational factors in their intent to participate in shared decision-making. The three main categories include 1) an individual’s representation of medically uncertainty, 2) how the individual copes with medical uncertainty, and 3) the individual’s behavioral intent to seek information and participate in shared decision-making during times of medically uncertain situations.

The SanaViz: Human Centered Geovisualization to facilitate visual exploration of Public Health data

These three manuscripts are presented as a PhD dissertation for the study of using GeoVis application to evaluate telehealth programs. The primary reason of this research was to understand how the GeoVis applications can be designed and developed using combined approaches of HC approach and cognitive fit theory and in terms utilized to evaluate telehealth program in Brazil. First manuscript The first manuscript in this dissertation presented a background about the use of GeoVisualization to facilitate visual exploration of public health data. The manuscript covered the existing challenges that were associated with an adoption of existing GeoVis applications. The manuscript combines the principles of Human Centered approach and Cognitive Fit Theory and a framework using a combination of these approaches is developed that lays the foundation of this research. The framework is then utilized to propose the design, development and evaluation of “the SanaViz” to evaluate telehealth data in Brazil, as a proof of concept. Second manuscript The second manuscript is a methods paper that describes the approaches that can be employed to design and develop “the SanaViz” based on the proposed framework. By defining the various elements of the HC approach and CFT, a mixed methods approach is utilized for the card sorting and sketching techniques. A representative sample of 20 study participants currently involved in the telehealth program at the NUTES telehealth center at UFPE, Recife, Brazil was enrolled. The findings of this manuscript helped us understand the needs of the diverse group of telehealth users, the tasks that they perform and helped us determine the essential features that might be necessary to be included in the proposed GeoVis application “the SanaViz”. Third manuscript The third manuscript involved mix- methods approach to compare the effectiveness and usefulness of the HC GeoVis application “the SanaViz” against a conventional GeoVis application “Instant Atlas”. The same group of 20 study participants who had earlier participated during Aim 2 was enrolled and a combination of quantitative and qualitative assessments was done. Effectiveness was gauged by the time that the participants took to complete the tasks using both the GeoVis applications, the ease with which they completed the tasks and the number of attempts that were taken to complete each task. Usefulness was assessed by System Usability Scale (SUS), a validated questionnaire tested in prior studies. In-depth interviews were conducted to gather opinions about both the GeoVis applications. This manuscript helped us in the demonstration of the usefulness and effectiveness of HC GeoVis applications to facilitate visual exploration of telehealth data, as a proof of concept. Together, these three manuscripts represent challenges of combining principles of Human Centered approach, Cognitive Fit Theory to design and develop GeoVis applications as a method to evaluate Telehealth data. To our knowledge, this is the first study to explore the usefulness and effectiveness of GeoVis to facilitate visual exploration of telehealth data. The results of the research enabled us to develop a framework for the design and development of GeoVis applications related to the areas of public health and especially telehealth. The results of our study showed that the varied users were involved with the telehealth program and the tasks that they performed. Further it enabled us to identify the components that might be essential to be included in these GeoVis applications. The results of our research answered the following questions; (a) Telehealth users vary in their level of understanding about GeoVis (b) Interaction features such as zooming, sorting, and linking and multiple views and representation features such as bar chart and choropleth maps were considered the most essential features of the GeoVis applications. (c) Comparing and sorting were two important tasks that the telehealth users would perform for exploratory data analysis. (d) A HC GeoVis prototype application is more effective and useful for exploration of telehealth data than a conventional GeoVis application. Future studies should be done to incorporate the proposed HC GeoVis framework to enable comprehensive assessment of the users and the tasks they perform to identify the features that might be necessary to be a part of the GeoVis applications. The results of this study demonstrate a novel approach to comprehensively and systematically enhance the evaluation of telehealth programs using the proposed GeoVis Framework.

Baseline evaluation of environmental physical activity and nutrition support of Texas Medical Center hospital worksites

Background: Obesity is a major health problem in the United States that has reached epidemic proportions. With most U.S adults spending the majority of their waking hours at work, the influence of the workplace environment on obesity is gaining in importance. Recent research implicates worksites as providing an 'obesogenic' environment as they encourage overeating and reduce the opportunity for physical activity. Objective: The aim of this study is to describe the nutrition and physical activity environment of Texas Medical Center (TMC) hospitals participating in the Shape Up Houston evaluation study to develop a scoring system to quantify the environmental data collected using the Environmental Assessment Tool (EAT) survey and to assess the inter-observer reliability of using the EAT survey. Methods: A survey instrument that was adapted from the Environmental Assessment Tool (EAT) developed by Dejoy DM et al in 2008 to measure the hospital environmental support for nutrition and physical activity was used for this study. The inter-observer reliability of using the EAT survey was measured and total percent agreement scores were computed. Most responses on the EAT survey are dichotomous (Yes and No) and these responses were coded with a '0' for a 'no' response and a '1' for a 'yes' response. A summative scoring system was developed to quantify these responses. Each hospital was given a score for each scale and subscale on the EAT survey in addition to a total score. All analyses were conducted using Stata 11 software. Results: High inter-observer reliability is observed using EAT. The percentage agreement scores ranged from 94.4%–100%. Only 2 of the 5 hospitals had a fitness facility onsite and scores for exercise programs and outdoor facilities available for hospital employees ranged from 0–62% and 0–37.5%, respectively. The healthy eating percentage for hospital cafeterias range from 42%–92% across the different hospitals while the healthy vending scores were 0%–40%. The total TMC 'healthy hospital' score was 49%. Conclusion: The EAT survey is a reliable instrument for measuring the physical activity and nutrition support environment of hospital worksites. The study results showed a large variability among the TMC hospitals in the existing physical activity and nutrition support environment. This study proposes cost effective policy changes that can increase environmental support to healthy eating and active living among TMC hospital employees.^